2021
Reis, Marcus; Gusev, Filipp; Taylor, Nicholas G.; Chung, Sang Hun; Verber, Matthew D.; Lee, Yueh Z.; Isayev, Olexandr; Leibfarth, Frank A.
Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis Journal Article
In: J. Am. Chem. Soc., vol. 143, no. 42, pp. 17677–17689, 2021, ISSN: 1520-5126.
Abstract | Links | BibTeX | Tags: Materials informatics, Science automation
@article{Reis2021,
title = {Machine-Learning-Guided Discovery of ^{19}F MRI Agents Enabled by Automated Copolymer Synthesis},
author = {Marcus Reis and Filipp Gusev and Nicholas G. Taylor and Sang Hun Chung and Matthew D. Verber and Yueh Z. Lee and Olexandr Isayev and Frank A. Leibfarth},
doi = {10.1021/jacs.1c08181},
issn = {1520-5126},
year = {2021},
date = {2021-10-27},
urldate = {2021-10-27},
journal = {J. Am. Chem. Soc.},
volume = {143},
number = {42},
pages = {17677--17689},
publisher = {American Chemical Society (ACS)},
abstract = {Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure\textendashproperty relationships. To tackle this challenge in the context of 19F magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental\textendashcomputational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring \<0.9% of the overall compositional space, lead to the identification of \>10 copolymer compositions that outperformed state-of-the-art materials.},
keywords = {Materials informatics, Science automation},
pubstate = {published},
tppubtype = {article}
}
Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure–property relationships. To tackle this challenge in the context of 19F magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental–computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring <0.9% of the overall compositional space, lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials.